Overview

Dataset statistics

Number of variables30
Number of observations449001
Missing cells0
Missing cells (%)0.0%
Duplicate rows115160
Duplicate rows (%)25.6%
Total size in memory102.8 MiB
Average record size in memory240.0 B

Variable types

Numeric19
Categorical11

Warnings

Dataset has 115160 (25.6%) duplicate rows Duplicates
first_browser has a high cardinality: 51 distinct values High cardinality
days_from_first_active_until_booking is highly correlated with days_from_account_created_until_first_booking and 1 other fieldsHigh correlation
days_from_account_created_until_first_booking is highly correlated with days_from_first_active_until_booking and 1 other fieldsHigh correlation
year_first_active is highly correlated with year_first_created_accountHigh correlation
month_first_active is highly correlated with weekodyear_first_active and 2 other fieldsHigh correlation
day_first_active is highly correlated with day_first_created_accountHigh correlation
dayofweek_first_active is highly correlated with dayofweek_first_created_accountHigh correlation
weekodyear_first_active is highly correlated with month_first_active and 2 other fieldsHigh correlation
year_first_booking is highly correlated with days_from_first_active_until_booking and 1 other fieldsHigh correlation
month_first_booking is highly correlated with weekofyear_first_bookingHigh correlation
weekofyear_first_booking is highly correlated with month_first_bookingHigh correlation
year_first_created_account is highly correlated with year_first_activeHigh correlation
month_first_created_account is highly correlated with month_first_active and 2 other fieldsHigh correlation
day_first_created_account is highly correlated with day_first_activeHigh correlation
dayofweek_first_created_account is highly correlated with dayofweek_first_activeHigh correlation
weekofyear_first_created_account is highly correlated with month_first_active and 2 other fieldsHigh correlation
days_from_first_active_until_account_created is highly skewed (γ1 = 80.18747491) Skewed
signup_flow has 355794 (79.2%) zeros Zeros
days_from_first_active_until_booking has 88175 (19.6%) zeros Zeros
days_from_first_active_until_account_created has 448462 (99.9%) zeros Zeros
days_from_account_created_until_first_booking has 88151 (19.6%) zeros Zeros
dayofweek_first_active has 71084 (15.8%) zeros Zeros
dayofweek_first_booking has 45816 (10.2%) zeros Zeros
dayofweek_first_created_account has 71066 (15.8%) zeros Zeros

Reproduction

Analysis started2021-05-17 23:07:18.988939
Analysis finished2021-05-17 23:09:44.654825
Duration2 minutes and 25.67 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

age
Real number (ℝ≥0)

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.72022557
Minimum16
Maximum115
Zeros0
Zeros (%)0.0%
Memory size3.4 MiB
2021-05-17T20:09:44.764015image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile24
Q131
median41
Q349
95-th percentile57
Maximum115
Range99
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.35364801
Coefficient of variation (CV)0.3033786734
Kurtosis4.422704702
Mean40.72022557
Median Absolute Deviation (MAD)8
Skewness1.164537793
Sum18283422
Variance152.6126192
MonotocityNot monotonic
2021-05-17T20:09:44.897836image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49130218
29.0%
3016216
 
3.6%
3116200
 
3.6%
3215506
 
3.5%
2915422
 
3.4%
2815049
 
3.4%
3314172
 
3.2%
2713731
 
3.1%
3412982
 
2.9%
3512246
 
2.7%
Other values (89)187259
41.7%
ValueCountFrequency (%)
1626
 
< 0.1%
1770
 
< 0.1%
181156
0.3%
192211
0.5%
201499
0.3%
ValueCountFrequency (%)
11512
 
< 0.1%
1134
 
< 0.1%
1121
 
< 0.1%
1112
 
< 0.1%
110186
< 0.1%

signup_flow
Real number (ℝ≥0)

ZEROS

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.286484885
Minimum0
Maximum25
Zeros355794
Zeros (%)79.2%
Memory size3.4 MiB
2021-05-17T20:09:45.010609image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile24
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.351544869
Coefficient of variation (CV)2.777864358
Kurtosis7.028920144
Mean2.286484885
Median Absolute Deviation (MAD)0
Skewness2.911430805
Sum1026634
Variance40.34212222
MonotocityNot monotonic
2021-05-17T20:09:45.121627image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0355794
79.2%
2517066
 
3.8%
116152
 
3.6%
216124
 
3.6%
310413
 
2.3%
129977
 
2.2%
247419
 
1.7%
234033
 
0.9%
51201
 
0.3%
61153
 
0.3%
Other values (16)9669
 
2.2%
ValueCountFrequency (%)
0355794
79.2%
116152
 
3.6%
216124
 
3.6%
310413
 
2.3%
41063
 
0.2%
ValueCountFrequency (%)
2517066
3.8%
247419
1.7%
234033
 
0.9%
22435
 
0.1%
21641
 
0.1%

days_from_first_active_until_booking
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2035
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1577.190367
Minimum0
Maximum7393
Zeros88175
Zeros (%)19.6%
Memory size3.4 MiB
2021-05-17T20:09:45.244260image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median13
Q35486
95-th percentile6051
Maximum7393
Range7393
Interquartile range (IQR)5485

Descriptive statistics

Standard deviation2549.174301
Coefficient of variation (CV)1.616275597
Kurtosis-0.848772277
Mean1577.190367
Median Absolute Deviation (MAD)13
Skewness1.062757598
Sum708160052
Variance6498289.619
MonotocityNot monotonic
2021-05-17T20:09:45.373233image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
088175
 
19.6%
149673
 
11.1%
221960
 
4.9%
313794
 
3.1%
49924
 
2.2%
58037
 
1.8%
66794
 
1.5%
75829
 
1.3%
85011
 
1.1%
93950
 
0.9%
Other values (2025)235854
52.5%
ValueCountFrequency (%)
088175
19.6%
149673
11.1%
221960
 
4.9%
313794
 
3.1%
49924
 
2.2%
ValueCountFrequency (%)
73931
< 0.1%
73281
< 0.1%
71012
< 0.1%
70991
< 0.1%
70952
< 0.1%

days_from_first_active_until_account_created
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct263
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1768214325
Minimum0
Maximum1456
Zeros448462
Zeros (%)99.9%
Memory size3.4 MiB
2021-05-17T20:09:45.507151image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1456
Range1456
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.408851282
Coefficient of variation (CV)53.21103415
Kurtosis7972.913828
Mean0.1768214325
Median Absolute Deviation (MAD)0
Skewness80.18747491
Sum79393
Variance88.52648244
MonotocityNot monotonic
2021-05-17T20:09:45.633816image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0448462
99.9%
153
 
< 0.1%
238
 
< 0.1%
425
 
< 0.1%
325
 
< 0.1%
57
 
< 0.1%
206
 
< 0.1%
186
 
< 0.1%
96
 
< 0.1%
115
 
< 0.1%
Other values (253)368
 
0.1%
ValueCountFrequency (%)
0448462
99.9%
153
 
< 0.1%
238
 
< 0.1%
325
 
< 0.1%
425
 
< 0.1%
ValueCountFrequency (%)
14561
< 0.1%
13691
< 0.1%
13611
< 0.1%
11481
< 0.1%
10361
< 0.1%

days_from_account_created_until_first_booking
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct2021
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1577.013265
Minimum-349
Maximum7101
Zeros88151
Zeros (%)19.6%
Memory size3.4 MiB
2021-05-17T20:09:45.767223image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-349
5-th percentile0
Q11
median13
Q35486
95-th percentile6051
Maximum7101
Range7450
Interquartile range (IQR)5485

Descriptive statistics

Standard deviation2549.161492
Coefficient of variation (CV)1.616448985
Kurtosis-0.848810614
Mean1577.013265
Median Absolute Deviation (MAD)13
Skewness1.0627727
Sum708080533
Variance6498224.312
MonotocityNot monotonic
2021-05-17T20:09:45.901370image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
088151
 
19.6%
149648
 
11.1%
221951
 
4.9%
313790
 
3.1%
49916
 
2.2%
58041
 
1.8%
66796
 
1.5%
75829
 
1.3%
85012
 
1.1%
93949
 
0.9%
Other values (2011)235918
52.5%
ValueCountFrequency (%)
-3491
< 0.1%
-3471
< 0.1%
-3381
< 0.1%
-3081
< 0.1%
-2981
< 0.1%
ValueCountFrequency (%)
71012
< 0.1%
70991
< 0.1%
70952
< 0.1%
70941
< 0.1%
70921
< 0.1%

year_first_active
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.83694
Minimum2009
Maximum2014
Zeros0
Zeros (%)0.0%
Memory size3.4 MiB
2021-05-17T20:09:46.007920image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2011
Q12012
median2013
Q32014
95-th percentile2014
Maximum2014
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9311603293
Coefficient of variation (CV)0.0004626109104
Kurtosis-0.2291199538
Mean2012.83694
Median Absolute Deviation (MAD)1
Skewness-0.5056026116
Sum903765799
Variance0.8670595589
MonotocityNot monotonic
2021-05-17T20:09:46.100688image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2013180498
40.2%
2014118230
26.3%
2012113711
25.3%
201131960
 
7.1%
20104595
 
1.0%
20097
 
< 0.1%
ValueCountFrequency (%)
20097
 
< 0.1%
20104595
 
1.0%
201131960
 
7.1%
2012113711
25.3%
2013180498
40.2%
ValueCountFrequency (%)
2014118230
26.3%
2013180498
40.2%
2012113711
25.3%
201131960
 
7.1%
20104595
 
1.0%

month_first_active
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.839652918
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size3.4 MiB
2021-05-17T20:09:46.213734image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.103485953
Coefficient of variation (CV)0.5314504126
Kurtosis-0.871141908
Mean5.839652918
Median Absolute Deviation (MAD)2
Skewness0.2899287923
Sum2622010
Variance9.631625059
MonotocityNot monotonic
2021-05-17T20:09:46.309763image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
560739
13.5%
658231
13.0%
449437
11.0%
343363
9.7%
236558
8.1%
135604
7.9%
833108
7.4%
932381
7.2%
727894
6.2%
1026716
6.0%
Other values (2)44970
10.0%
ValueCountFrequency (%)
135604
7.9%
236558
8.1%
343363
9.7%
449437
11.0%
560739
13.5%
ValueCountFrequency (%)
1218991
4.2%
1125979
5.8%
1026716
6.0%
932381
7.2%
833108
7.4%

day_first_active
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.65494954
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size3.4 MiB
2021-05-17T20:09:46.413914image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.55294633
Coefficient of variation (CV)0.546341354
Kurtosis-1.185728864
Mean15.65494954
Median Absolute Deviation (MAD)7
Skewness-0.00295224836
Sum7029088
Variance73.15289092
MonotocityNot monotonic
2021-05-17T20:09:46.529301image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2416169
 
3.6%
1615824
 
3.5%
2315656
 
3.5%
1315541
 
3.5%
2715523
 
3.5%
515463
 
3.4%
2215441
 
3.4%
2615409
 
3.4%
615384
 
3.4%
1715355
 
3.4%
Other values (21)293236
65.3%
ValueCountFrequency (%)
111461
2.6%
213467
3.0%
315129
3.4%
414900
3.3%
515463
3.4%
ValueCountFrequency (%)
314073
 
0.9%
3011030
2.5%
2913077
2.9%
2815065
3.4%
2715523
3.5%

dayofweek_first_active
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.540446458
Minimum0
Maximum6
Zeros71084
Zeros (%)15.8%
Memory size3.4 MiB
2021-05-17T20:09:46.645299image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.814653128
Coefficient of variation (CV)0.7143048113
Kurtosis-0.993171433
Mean2.540446458
Median Absolute Deviation (MAD)1
Skewness0.2567032416
Sum1140663
Variance3.292965974
MonotocityNot monotonic
2021-05-17T20:09:46.732343image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
181453
18.1%
280758
18.0%
373622
16.4%
071084
15.8%
463332
14.1%
549012
10.9%
629740
 
6.6%
ValueCountFrequency (%)
071084
15.8%
181453
18.1%
280758
18.0%
373622
16.4%
463332
14.1%
ValueCountFrequency (%)
629740
 
6.6%
549012
10.9%
463332
14.1%
373622
16.4%
280758
18.0%

weekodyear_first_active
Real number (ℝ≥0)

HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.9726259
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Memory size3.4 MiB
2021-05-17T20:09:47.201802image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q114
median22
Q334
95-th percentile48
Maximum53
Range52
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.45303375
Coefficient of variation (CV)0.5611831514
Kurtosis-0.8735427894
Mean23.9726259
Median Absolute Deviation (MAD)10
Skewness0.2870609696
Sum10763733
Variance180.984117
MonotocityNot monotonic
2021-05-17T20:09:47.342835image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2114399
 
3.2%
2314270
 
3.2%
2614180
 
3.2%
2013986
 
3.1%
2513935
 
3.1%
1913881
 
3.1%
2413617
 
3.0%
2212674
 
2.8%
1812404
 
2.8%
1711884
 
2.6%
Other values (43)313771
69.9%
ValueCountFrequency (%)
15444
1.2%
26281
1.4%
38284
1.8%
48328
1.9%
57808
1.7%
ValueCountFrequency (%)
533
 
< 0.1%
523514
0.8%
514530
1.0%
505233
1.2%
496059
1.3%

year_first_booking
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.150717
Minimum2010
Maximum2029
Zeros0
Zeros (%)0.0%
Memory size3.4 MiB
2021-05-17T20:09:47.455759image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2011
Q12013
median2013
Q32029
95-th percentile2029
Maximum2029
Range19
Interquartile range (IQR)16

Descriptive statistics

Standard deviation7.194494414
Coefficient of variation (CV)0.003566661804
Kurtosis-0.9122288994
Mean2017.150717
Median Absolute Deviation (MAD)1
Skewness1.010640064
Sum905702689
Variance51.76074987
MonotocityNot monotonic
2021-05-17T20:09:47.540024image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2013131311
29.2%
2029119810
26.7%
201285214
19.0%
201482630
18.4%
201123488
 
5.2%
20103364
 
0.7%
20153184
 
0.7%
ValueCountFrequency (%)
20103364
 
0.7%
201123488
 
5.2%
201285214
19.0%
2013131311
29.2%
201482630
18.4%
ValueCountFrequency (%)
2029119810
26.7%
20153184
 
0.7%
201482630
18.4%
2013131311
29.2%
201285214
19.0%

month_first_booking
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.915367672
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size3.4 MiB
2021-05-17T20:09:47.638874image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q37
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.546033192
Coefficient of variation (CV)0.4304099649
Kurtosis-0.04900599078
Mean5.915367672
Median Absolute Deviation (MAD)1
Skewness0.2382102987
Sum2656006
Variance6.482285015
MonotocityNot monotonic
2021-05-17T20:09:47.732083image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6163800
36.5%
546015
 
10.2%
434774
 
7.7%
330768
 
6.9%
827246
 
6.1%
226223
 
5.8%
725649
 
5.7%
924583
 
5.5%
121860
 
4.9%
1020211
 
4.5%
Other values (2)27872
 
6.2%
ValueCountFrequency (%)
121860
4.9%
226223
5.8%
330768
6.9%
434774
7.7%
546015
10.2%
ValueCountFrequency (%)
129965
 
2.2%
1117907
4.0%
1020211
4.5%
924583
5.5%
827246
6.1%

day_first_booking
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.19065882
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size3.4 MiB
2021-05-17T20:09:47.837929image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median15
Q320
95-th percentile28
Maximum31
Range30
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.196865264
Coefficient of variation (CV)0.4737691334
Kurtosis-0.4890551126
Mean15.19065882
Median Absolute Deviation (MAD)5
Skewness0.06491052359
Sum6820621
Variance51.79486962
MonotocityNot monotonic
2021-05-17T20:09:47.946103image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
15131787
29.4%
1011949
 
2.7%
1711739
 
2.6%
511703
 
2.6%
1611658
 
2.6%
911647
 
2.6%
1411626
 
2.6%
1111553
 
2.6%
711416
 
2.5%
2011408
 
2.5%
Other values (21)212515
47.3%
ValueCountFrequency (%)
19022
2.0%
210238
2.3%
311056
2.5%
411342
2.5%
511703
2.6%
ValueCountFrequency (%)
311969
 
0.4%
306331
1.4%
298371
1.9%
289784
2.2%
2710386
2.3%

dayofweek_first_booking
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.912866564
Minimum0
Maximum6
Zeros45816
Zeros (%)10.2%
Memory size3.4 MiB
2021-05-17T20:09:48.044610image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.608861927
Coefficient of variation (CV)0.5523294294
Kurtosis-0.8588803838
Mean2.912866564
Median Absolute Deviation (MAD)1
Skewness-0.3349162373
Sum1307880
Variance2.588436701
MonotocityNot monotonic
2021-05-17T20:09:48.131040image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4168918
37.6%
263796
 
14.2%
161050
 
13.6%
359395
 
13.2%
045816
 
10.2%
534775
 
7.7%
615251
 
3.4%
ValueCountFrequency (%)
045816
 
10.2%
161050
 
13.6%
263796
 
14.2%
359395
 
13.2%
4168918
37.6%
ValueCountFrequency (%)
615251
 
3.4%
534775
 
7.7%
4168918
37.6%
359395
 
13.2%
263796
 
14.2%

weekofyear_first_booking
Real number (ℝ≥0)

HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.19333587
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Memory size3.4 MiB
2021-05-17T20:09:48.246369image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q118
median24
Q329
95-th percentile46
Maximum53
Range52
Interquartile range (IQR)11

Descriptive statistics

Standard deviation11.07113996
Coefficient of variation (CV)0.4576111381
Kurtosis-0.03836883954
Mean24.19333587
Median Absolute Deviation (MAD)6
Skewness0.2934235302
Sum10862832
Variance122.5701401
MonotocityNot monotonic
2021-05-17T20:09:48.380534image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24130650
29.1%
2111157
 
2.5%
2310838
 
2.4%
1910401
 
2.3%
2610383
 
2.3%
2510188
 
2.3%
2010118
 
2.3%
229411
 
2.1%
188967
 
2.0%
148213
 
1.8%
Other values (43)228675
50.9%
ValueCountFrequency (%)
12898
0.6%
23542
0.8%
35566
1.2%
45133
1.1%
55092
1.1%
ValueCountFrequency (%)
531
 
< 0.1%
521463
 
0.3%
512660
0.6%
503404
0.8%
493832
0.9%

year_first_created_account
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
2013
180521 
2014
118264 
2012
113710 
2011
31932 
2010
 
4574

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1796004
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2010
2nd row2011
3rd row2010
4th row2011
5th row2010
ValueCountFrequency (%)
2013180521
40.2%
2014118264
26.3%
2012113710
25.3%
201131932
 
7.1%
20104574
 
1.0%
2021-05-17T20:09:48.601864image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-17T20:09:48.665211image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2013180521
40.2%
2014118264
26.3%
2012113710
25.3%
201131932
 
7.1%
20104574
 
1.0%

Most occurring characters

ValueCountFrequency (%)
2562711
31.3%
1480933
26.8%
0453575
25.3%
3180521
 
10.1%
4118264
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1796004
100.0%

Most frequent character per category

ValueCountFrequency (%)
2562711
31.3%
1480933
26.8%
0453575
25.3%
3180521
 
10.1%
4118264
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common1796004
100.0%

Most frequent character per script

ValueCountFrequency (%)
2562711
31.3%
1480933
26.8%
0453575
25.3%
3180521
 
10.1%
4118264
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1796004
100.0%

Most frequent character per block

ValueCountFrequency (%)
2562711
31.3%
1480933
26.8%
0453575
25.3%
3180521
 
10.1%
4118264
 
6.6%

month_first_created_account
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.84047697
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size3.4 MiB
2021-05-17T20:09:48.740054image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.103921653
Coefficient of variation (CV)0.5314500287
Kurtosis-0.871861039
Mean5.84047697
Median Absolute Deviation (MAD)2
Skewness0.2894684173
Sum2622380
Variance9.634329629
MonotocityNot monotonic
2021-05-17T20:09:48.830762image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
560735
13.5%
658201
13.0%
449422
11.0%
343341
9.7%
236550
8.1%
135614
7.9%
833134
7.4%
932389
7.2%
727881
6.2%
1026735
6.0%
Other values (2)44999
10.0%
ValueCountFrequency (%)
135614
7.9%
236550
8.1%
343341
9.7%
449422
11.0%
560735
13.5%
ValueCountFrequency (%)
1218995
4.2%
1126004
5.8%
1026735
6.0%
932389
7.2%
833134
7.4%

day_first_created_account
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.65586936
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size3.4 MiB
2021-05-17T20:09:48.933830image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.553639694
Coefficient of variation (CV)0.5463535429
Kurtosis-1.186015732
Mean15.65586936
Median Absolute Deviation (MAD)7
Skewness-0.003144012124
Sum7029501
Variance73.16475201
MonotocityNot monotonic
2021-05-17T20:09:49.045202image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2416183
 
3.6%
1615841
 
3.5%
2315637
 
3.5%
2715532
 
3.5%
1315526
 
3.5%
515462
 
3.4%
2215450
 
3.4%
2615414
 
3.4%
615370
 
3.4%
1715350
 
3.4%
Other values (21)293236
65.3%
ValueCountFrequency (%)
111459
2.6%
213469
3.0%
315138
3.4%
414903
3.3%
515462
3.4%
ValueCountFrequency (%)
314071
 
0.9%
3011027
2.5%
2913075
2.9%
2815092
3.4%
2715532
3.5%

dayofweek_first_created_account
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.540637994
Minimum0
Maximum6
Zeros71066
Zeros (%)15.8%
Memory size3.4 MiB
2021-05-17T20:09:49.146651image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.814766668
Coefficient of variation (CV)0.7142956503
Kurtosis-0.9935157852
Mean2.540637994
Median Absolute Deviation (MAD)1
Skewness0.2566643548
Sum1140749
Variance3.29337806
MonotocityNot monotonic
2021-05-17T20:09:49.233897image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
181478
18.1%
280757
18.0%
373572
16.4%
071066
15.8%
463346
14.1%
549035
10.9%
629747
 
6.6%
ValueCountFrequency (%)
071066
15.8%
181478
18.1%
280757
18.0%
373572
16.4%
463346
14.1%
ValueCountFrequency (%)
629747
 
6.6%
549035
10.9%
463346
14.1%
373572
16.4%
280757
18.0%

weekofyear_first_created_account
Real number (ℝ≥0)

HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.97627177
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Memory size3.4 MiB
2021-05-17T20:09:49.351171image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q114
median22
Q334
95-th percentile48
Maximum53
Range52
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.45547183
Coefficient of variation (CV)0.5611995041
Kurtosis-0.8743655565
Mean23.97627177
Median Absolute Deviation (MAD)10
Skewness0.2866583393
Sum10765370
Variance181.049722
MonotocityNot monotonic
2021-05-17T20:09:49.489834image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2114397
 
3.2%
2314272
 
3.2%
2614174
 
3.2%
2013977
 
3.1%
2513930
 
3.1%
1913880
 
3.1%
2413615
 
3.0%
2212676
 
2.8%
1812408
 
2.8%
1711883
 
2.6%
Other values (43)313789
69.9%
ValueCountFrequency (%)
15446
1.2%
26282
1.4%
38285
1.8%
48330
1.9%
57811
1.7%
ValueCountFrequency (%)
533
 
< 0.1%
523514
0.8%
514531
1.0%
505234
1.2%
496061
1.3%

gender
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
-unknown-
168301 
FEMALE
148478 
MALE
131314 
OTHER
 
908

Length

Max length9
Median length6
Mean length6.537564504
Min length4

Characters and Unicode

Total characters2935373
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-unknown-
2nd rowMALE
3rd rowFEMALE
4th rowFEMALE
5th row-unknown-
ValueCountFrequency (%)
-unknown-168301
37.5%
FEMALE148478
33.1%
MALE131314
29.2%
OTHER908
 
0.2%
2021-05-17T20:09:49.721101image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-17T20:09:49.791441image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
unknown168301
37.5%
female148478
33.1%
male131314
29.2%
other908
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n504903
17.2%
E429178
14.6%
-336602
11.5%
M279792
9.5%
A279792
9.5%
L279792
9.5%
u168301
 
5.7%
k168301
 
5.7%
o168301
 
5.7%
w168301
 
5.7%
Other values (5)152110
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1420664
48.4%
Lowercase Letter1178107
40.1%
Dash Punctuation336602
 
11.5%

Most frequent character per category

ValueCountFrequency (%)
E429178
30.2%
M279792
19.7%
A279792
19.7%
L279792
19.7%
F148478
 
10.5%
O908
 
0.1%
T908
 
0.1%
H908
 
0.1%
R908
 
0.1%
ValueCountFrequency (%)
n504903
42.9%
u168301
 
14.3%
k168301
 
14.3%
o168301
 
14.3%
w168301
 
14.3%
ValueCountFrequency (%)
-336602
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2598771
88.5%
Common336602
 
11.5%

Most frequent character per script

ValueCountFrequency (%)
n504903
19.4%
E429178
16.5%
M279792
10.8%
A279792
10.8%
L279792
10.8%
u168301
 
6.5%
k168301
 
6.5%
o168301
 
6.5%
w168301
 
6.5%
F148478
 
5.7%
Other values (4)3632
 
0.1%
ValueCountFrequency (%)
-336602
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2935373
100.0%

Most frequent character per block

ValueCountFrequency (%)
n504903
17.2%
E429178
14.6%
-336602
11.5%
M279792
9.5%
A279792
9.5%
L279792
9.5%
u168301
 
5.7%
k168301
 
5.7%
o168301
 
5.7%
w168301
 
5.7%
Other values (5)152110
 
5.2%

signup_method
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
basic
325738 
facebook
122574 
google
 
689

Length

Max length8
Median length5
Mean length5.820512649
Min length5

Characters and Unicode

Total characters2613416
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfacebook
2nd rowfacebook
3rd rowbasic
4th rowfacebook
5th rowbasic
ValueCountFrequency (%)
basic325738
72.5%
facebook122574
 
27.3%
google689
 
0.2%
2021-05-17T20:09:49.973459image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-17T20:09:50.042203image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
basic325738
72.5%
facebook122574
 
27.3%
google689
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a448312
17.2%
c448312
17.2%
b448312
17.2%
s325738
12.5%
i325738
12.5%
o246526
9.4%
e123263
 
4.7%
f122574
 
4.7%
k122574
 
4.7%
g1378
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2613416
100.0%

Most frequent character per category

ValueCountFrequency (%)
a448312
17.2%
c448312
17.2%
b448312
17.2%
s325738
12.5%
i325738
12.5%
o246526
9.4%
e123263
 
4.7%
f122574
 
4.7%
k122574
 
4.7%
g1378
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin2613416
100.0%

Most frequent character per script

ValueCountFrequency (%)
a448312
17.2%
c448312
17.2%
b448312
17.2%
s325738
12.5%
i325738
12.5%
o246526
9.4%
e123263
 
4.7%
f122574
 
4.7%
k122574
 
4.7%
g1378
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2613416
100.0%

Most frequent character per block

ValueCountFrequency (%)
a448312
17.2%
c448312
17.2%
b448312
17.2%
s325738
12.5%
i325738
12.5%
o246526
9.4%
e123263
 
4.7%
f122574
 
4.7%
k122574
 
4.7%
g1378
 
0.1%

language
Categorical

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
en
436095 
fr
 
2565
zh
 
2186
de
 
1868
es
 
1740
Other values (20)
 
4547

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters898002
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen
ValueCountFrequency (%)
en436095
97.1%
fr2565
 
0.6%
zh2186
 
0.5%
de1868
 
0.4%
es1740
 
0.4%
ko1122
 
0.2%
it878
 
0.2%
ru620
 
0.1%
pt387
 
0.1%
ja354
 
0.1%
Other values (15)1186
 
0.3%
2021-05-17T20:09:50.224583image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
en436095
97.1%
fr2565
 
0.6%
zh2186
 
0.5%
de1868
 
0.4%
es1740
 
0.4%
ko1122
 
0.2%
it878
 
0.2%
ru620
 
0.1%
pt387
 
0.1%
ja354
 
0.1%
Other values (15)1186
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e439760
49.0%
n436475
48.6%
r3275
 
0.4%
f2585
 
0.3%
h2235
 
0.2%
z2186
 
0.2%
s2024
 
0.2%
d1987
 
0.2%
t1380
 
0.2%
o1177
 
0.1%
Other values (9)4918
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter898002
100.0%

Most frequent character per category

ValueCountFrequency (%)
e439760
49.0%
n436475
48.6%
r3275
 
0.4%
f2585
 
0.3%
h2235
 
0.2%
z2186
 
0.2%
s2024
 
0.2%
d1987
 
0.2%
t1380
 
0.2%
o1177
 
0.1%
Other values (9)4918
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin898002
100.0%

Most frequent character per script

ValueCountFrequency (%)
e439760
49.0%
n436475
48.6%
r3275
 
0.4%
f2585
 
0.3%
h2235
 
0.2%
z2186
 
0.2%
s2024
 
0.2%
d1987
 
0.2%
t1380
 
0.2%
o1177
 
0.1%
Other values (9)4918
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII898002
100.0%

Most frequent character per block

ValueCountFrequency (%)
e439760
49.0%
n436475
48.6%
r3275
 
0.4%
f2585
 
0.3%
h2235
 
0.2%
z2186
 
0.2%
s2024
 
0.2%
d1987
 
0.2%
t1380
 
0.2%
o1177
 
0.1%
Other values (9)4918
 
0.5%
Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
direct
297015 
sem-brand
58469 
sem-non-brand
40397 
seo
 
18825
other
 
14078
Other values (3)
 
20217

Length

Max length13
Median length6
Mean length6.821588371
Min length3

Characters and Unicode

Total characters3062900
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdirect
2nd rowseo
3rd rowdirect
4th rowdirect
5th rowother
ValueCountFrequency (%)
direct297015
66.2%
sem-brand58469
 
13.0%
sem-non-brand40397
 
9.0%
seo18825
 
4.2%
other14078
 
3.1%
api12110
 
2.7%
content5736
 
1.3%
remarketing2371
 
0.5%
2021-05-17T20:09:50.418940image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-17T20:09:50.490834image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
direct297015
66.2%
sem-brand58469
 
13.0%
sem-non-brand40397
 
9.0%
seo18825
 
4.2%
other14078
 
3.1%
api12110
 
2.7%
content5736
 
1.3%
remarketing2371
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e439262
14.3%
r414701
13.5%
d395881
12.9%
t324936
10.6%
i311496
10.2%
c302751
9.9%
n193503
6.3%
-139263
 
4.5%
s117691
 
3.8%
a113347
 
3.7%
Other values (7)310069
10.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2923637
95.5%
Dash Punctuation139263
 
4.5%

Most frequent character per category

ValueCountFrequency (%)
e439262
15.0%
r414701
14.2%
d395881
13.5%
t324936
11.1%
i311496
10.7%
c302751
10.4%
n193503
6.6%
s117691
 
4.0%
a113347
 
3.9%
m101237
 
3.5%
Other values (6)208832
7.1%
ValueCountFrequency (%)
-139263
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2923637
95.5%
Common139263
 
4.5%

Most frequent character per script

ValueCountFrequency (%)
e439262
15.0%
r414701
14.2%
d395881
13.5%
t324936
11.1%
i311496
10.7%
c302751
10.4%
n193503
6.6%
s117691
 
4.0%
a113347
 
3.9%
m101237
 
3.5%
Other values (6)208832
7.1%
ValueCountFrequency (%)
-139263
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3062900
100.0%

Most frequent character per block

ValueCountFrequency (%)
e439262
14.3%
r414701
13.5%
d395881
12.9%
t324936
10.6%
i311496
10.2%
c302751
9.9%
n193503
6.3%
-139263
 
4.5%
s117691
 
3.8%
a113347
 
3.7%
Other values (7)310069
10.1%
Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
direct
296322 
google
112171 
other
 
19559
craigslist
 
5332
bing
 
5150
Other values (13)
 
10467

Length

Max length19
Median length6
Mean length6.031271645
Min length3

Characters and Unicode

Total characters2708047
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdirect
2nd rowgoogle
3rd rowdirect
4th rowdirect
5th rowother
ValueCountFrequency (%)
direct296322
66.0%
google112171
 
25.0%
other19559
 
4.4%
craigslist5332
 
1.2%
bing5150
 
1.1%
facebook4767
 
1.1%
vast1424
 
0.3%
padmapper1064
 
0.2%
facebook-open-graph1001
 
0.2%
yahoo828
 
0.2%
Other values (8)1383
 
0.3%
2021-05-17T20:09:50.725545image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
direct296322
66.0%
google112171
 
25.0%
other19559
 
4.4%
craigslist5332
 
1.2%
bing5150
 
1.1%
facebook4767
 
1.1%
vast1424
 
0.3%
padmapper1064
 
0.2%
facebook-open-graph1001
 
0.2%
yahoo828
 
0.2%
Other values (8)1383
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e437359
16.2%
r323609
11.9%
t323327
11.9%
i312687
11.5%
c307422
11.4%
d297449
11.0%
o258094
9.5%
g236624
8.7%
l117757
 
4.3%
h21388
 
0.8%
Other values (14)72331
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2705791
99.9%
Dash Punctuation2256
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e437359
16.2%
r323609
12.0%
t323327
11.9%
i312687
11.6%
c307422
11.4%
d297449
11.0%
o258094
9.5%
g236624
8.7%
l117757
 
4.4%
h21388
 
0.8%
Other values (13)70075
 
2.6%
ValueCountFrequency (%)
-2256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2705791
99.9%
Common2256
 
0.1%

Most frequent character per script

ValueCountFrequency (%)
e437359
16.2%
r323609
12.0%
t323327
11.9%
i312687
11.6%
c307422
11.4%
d297449
11.0%
o258094
9.5%
g236624
8.7%
l117757
 
4.4%
h21388
 
0.8%
Other values (13)70075
 
2.6%
ValueCountFrequency (%)
-2256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2708047
100.0%

Most frequent character per block

ValueCountFrequency (%)
e437359
16.2%
r323609
11.9%
t323327
11.9%
i312687
11.5%
c307422
11.4%
d297449
11.0%
o258094
9.5%
g236624
8.7%
l117757
 
4.3%
h21388
 
0.8%
Other values (14)72331
 
2.7%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
untracked
241435 
linked
98697 
omg
94083 
tracked-other
 
10627
product
 
3885
Other values (2)
 
274

Length

Max length13
Median length9
Mean length7.160692292
Min length3

Characters and Unicode

Total characters3215158
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuntracked
2nd rowuntracked
3rd rowuntracked
4th rowuntracked
5th rowomg
ValueCountFrequency (%)
untracked241435
53.8%
linked98697
22.0%
omg94083
 
21.0%
tracked-other10627
 
2.4%
product3885
 
0.9%
marketing211
 
< 0.1%
local ops63
 
< 0.1%
2021-05-17T20:09:50.932176image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-17T20:09:51.000521image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
untracked241435
53.8%
linked98697
22.0%
omg94083
 
21.0%
tracked-other10627
 
2.4%
product3885
 
0.9%
marketing211
 
< 0.1%
local63
 
< 0.1%
ops63
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e361597
11.2%
d354644
11.0%
k350970
10.9%
n340343
10.6%
t266785
8.3%
r266785
8.3%
c256010
8.0%
a252336
7.8%
u245320
7.6%
o108721
 
3.4%
Other values (9)411647
12.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3204468
99.7%
Dash Punctuation10627
 
0.3%
Space Separator63
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e361597
11.3%
d354644
11.1%
k350970
11.0%
n340343
10.6%
t266785
8.3%
r266785
8.3%
c256010
8.0%
a252336
7.9%
u245320
7.7%
o108721
 
3.4%
Other values (7)400957
12.5%
ValueCountFrequency (%)
-10627
100.0%
ValueCountFrequency (%)
63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3204468
99.7%
Common10690
 
0.3%

Most frequent character per script

ValueCountFrequency (%)
e361597
11.3%
d354644
11.1%
k350970
11.0%
n340343
10.6%
t266785
8.3%
r266785
8.3%
c256010
8.0%
a252336
7.9%
u245320
7.7%
o108721
 
3.4%
Other values (7)400957
12.5%
ValueCountFrequency (%)
-10627
99.4%
63
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3215158
100.0%

Most frequent character per block

ValueCountFrequency (%)
e361597
11.2%
d354644
11.0%
k350970
10.9%
n340343
10.6%
t266785
8.3%
r266785
8.3%
c256010
8.0%
a252336
7.8%
u245320
7.6%
o108721
 
3.4%
Other values (9)411647
12.8%

signup_app
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
Web
404715 
iOS
 
27966
Moweb
 
9126
Android
 
7194

Length

Max length7
Median length3
Mean length3.104739188
Min length3

Characters and Unicode

Total characters1394031
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeb
2nd rowWeb
3rd rowWeb
4th rowWeb
5th rowWeb
ValueCountFrequency (%)
Web404715
90.1%
iOS27966
 
6.2%
Moweb9126
 
2.0%
Android7194
 
1.6%
2021-05-17T20:09:51.219452image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-17T20:09:51.291610image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
web404715
90.1%
ios27966
 
6.2%
moweb9126
 
2.0%
android7194
 
1.6%

Most occurring characters

ValueCountFrequency (%)
e413841
29.7%
b413841
29.7%
W404715
29.0%
i35160
 
2.5%
O27966
 
2.0%
S27966
 
2.0%
o16320
 
1.2%
d14388
 
1.0%
M9126
 
0.7%
w9126
 
0.7%
Other values (3)21582
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter917064
65.8%
Uppercase Letter476967
34.2%

Most frequent character per category

ValueCountFrequency (%)
e413841
45.1%
b413841
45.1%
i35160
 
3.8%
o16320
 
1.8%
d14388
 
1.6%
w9126
 
1.0%
n7194
 
0.8%
r7194
 
0.8%
ValueCountFrequency (%)
W404715
84.9%
O27966
 
5.9%
S27966
 
5.9%
M9126
 
1.9%
A7194
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Latin1394031
100.0%

Most frequent character per script

ValueCountFrequency (%)
e413841
29.7%
b413841
29.7%
W404715
29.0%
i35160
 
2.5%
O27966
 
2.0%
S27966
 
2.0%
o16320
 
1.2%
d14388
 
1.0%
M9126
 
0.7%
w9126
 
0.7%
Other values (3)21582
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1394031
100.0%

Most frequent character per block

ValueCountFrequency (%)
e413841
29.7%
b413841
29.7%
W404715
29.0%
i35160
 
2.5%
O27966
 
2.0%
S27966
 
2.0%
o16320
 
1.2%
d14388
 
1.0%
M9126
 
0.7%
w9126
 
0.7%
Other values (3)21582
 
1.5%
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
Mac Desktop
213221 
Windows Desktop
156216 
iPhone
32727 
iPad
30939 
Other/Unknown
 
6574
Other values (4)
 
9324

Length

Max length18
Median length11
Mean length11.63518344
Min length4

Characters and Unicode

Total characters5224209
Distinct characters30
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMac Desktop
2nd rowMac Desktop
3rd rowWindows Desktop
4th rowMac Desktop
5th rowMac Desktop
ValueCountFrequency (%)
Mac Desktop213221
47.5%
Windows Desktop156216
34.8%
iPhone32727
 
7.3%
iPad30939
 
6.9%
Other/Unknown6574
 
1.5%
Android Phone3820
 
0.9%
Desktop (Other)2854
 
0.6%
Android Tablet2553
 
0.6%
SmartPhone (Other)97
 
< 0.1%
2021-05-17T20:09:51.487439image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-17T20:09:51.581834image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
desktop372291
45.0%
mac213221
25.8%
windows156216
18.9%
iphone32727
 
4.0%
ipad30939
 
3.7%
other/unknown6574
 
0.8%
android6373
 
0.8%
phone3820
 
0.5%
other2951
 
0.4%
tablet2553
 
0.3%

Most occurring characters

ValueCountFrequency (%)
o578098
11.1%
s528507
 
10.1%
e421013
 
8.1%
t384466
 
7.4%
k378865
 
7.3%
378761
 
7.3%
D372291
 
7.1%
p372291
 
7.1%
a246810
 
4.7%
i226255
 
4.3%
Other values (20)1336852
25.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3998539
76.5%
Uppercase Letter834433
 
16.0%
Space Separator378761
 
7.3%
Other Punctuation6574
 
0.1%
Open Punctuation2951
 
0.1%
Close Punctuation2951
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
o578098
14.5%
s528507
13.2%
e421013
10.5%
t384466
9.6%
k378865
9.5%
p372291
9.3%
a246810
6.2%
i226255
 
5.7%
n218955
 
5.5%
c213221
 
5.3%
Other values (7)430058
10.8%
ValueCountFrequency (%)
D372291
44.6%
M213221
25.6%
W156216
18.7%
P67583
 
8.1%
O9525
 
1.1%
U6574
 
0.8%
A6373
 
0.8%
T2553
 
0.3%
S97
 
< 0.1%
ValueCountFrequency (%)
378761
100.0%
ValueCountFrequency (%)
/6574
100.0%
ValueCountFrequency (%)
(2951
100.0%
ValueCountFrequency (%)
)2951
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4832972
92.5%
Common391237
 
7.5%

Most frequent character per script

ValueCountFrequency (%)
o578098
12.0%
s528507
10.9%
e421013
 
8.7%
t384466
 
8.0%
k378865
 
7.8%
D372291
 
7.7%
p372291
 
7.7%
a246810
 
5.1%
i226255
 
4.7%
n218955
 
4.5%
Other values (16)1105421
22.9%
ValueCountFrequency (%)
378761
96.8%
/6574
 
1.7%
(2951
 
0.8%
)2951
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII5224209
100.0%

Most frequent character per block

ValueCountFrequency (%)
o578098
11.1%
s528507
 
10.1%
e421013
 
8.1%
t384466
 
7.4%
k378865
 
7.3%
378761
 
7.3%
D372291
 
7.1%
p372291
 
7.1%
a246810
 
4.7%
i226255
 
4.3%
Other values (20)1336852
25.6%

first_browser
Categorical

HIGH CARDINALITY

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
Chrome
145623 
Safari
105699 
Firefox
78029 
IE
43516 
Mobile Safari
38350 
Other values (46)
37784 

Length

Max length18
Median length6
Mean length6.672610974
Min length2

Characters and Unicode

Total characters2996009
Distinct characters50
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowChrome
2nd rowChrome
3rd rowIE
4th rowFirefox
5th rowChrome
ValueCountFrequency (%)
Chrome145623
32.4%
Safari105699
23.5%
Firefox78029
17.4%
IE43516
 
9.7%
Mobile Safari38350
 
8.5%
-unknown-32606
 
7.3%
Chrome Mobile2104
 
0.5%
Android Browser1293
 
0.3%
Opera376
 
0.1%
AOL Explorer351
 
0.1%
Other values (41)1054
 
0.2%
2021-05-17T20:09:51.908580image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chrome147727
30.1%
safari144049
29.3%
firefox78066
15.9%
ie43552
 
8.9%
mobile40529
 
8.2%
unknown32606
 
6.6%
browser1453
 
0.3%
android1293
 
0.3%
explorer395
 
0.1%
opera382
 
0.1%
Other values (45)1424
 
0.3%

Most occurring characters

ValueCountFrequency (%)
r375788
12.5%
o302773
10.1%
a288881
9.6%
e269094
 
9.0%
i264455
 
8.8%
f222115
 
7.4%
m148065
 
4.9%
h147970
 
4.9%
C147934
 
4.9%
S144339
 
4.8%
Other values (40)684595
22.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2384790
79.6%
Uppercase Letter503516
 
16.8%
Dash Punctuation65212
 
2.2%
Space Separator42475
 
1.4%
Other Punctuation12
 
< 0.1%
Decimal Number4
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
r375788
15.8%
o302773
12.7%
a288881
12.1%
e269094
11.3%
i264455
11.1%
f222115
9.3%
m148065
 
6.2%
h147970
 
6.2%
n99312
 
4.2%
x78560
 
3.3%
Other values (14)187777
7.9%
ValueCountFrequency (%)
C147934
29.4%
S144339
28.7%
F78088
15.5%
E43949
 
8.7%
I43624
 
8.7%
M40773
 
8.1%
B1775
 
0.4%
A1725
 
0.3%
O748
 
0.1%
L351
 
0.1%
Other values (10)210
 
< 0.1%
ValueCountFrequency (%)
02
50.0%
21
25.0%
71
25.0%
ValueCountFrequency (%)
-65212
100.0%
ValueCountFrequency (%)
42475
100.0%
ValueCountFrequency (%)
.12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2888306
96.4%
Common107703
 
3.6%

Most frequent character per script

ValueCountFrequency (%)
r375788
13.0%
o302773
10.5%
a288881
10.0%
e269094
9.3%
i264455
9.2%
f222115
 
7.7%
m148065
 
5.1%
h147970
 
5.1%
C147934
 
5.1%
S144339
 
5.0%
Other values (34)576892
20.0%
ValueCountFrequency (%)
-65212
60.5%
42475
39.4%
.12
 
< 0.1%
02
 
< 0.1%
21
 
< 0.1%
71
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2996009
100.0%

Most frequent character per block

ValueCountFrequency (%)
r375788
12.5%
o302773
10.1%
a288881
9.6%
e269094
 
9.0%
i264455
 
8.8%
f222115
 
7.4%
m148065
 
4.9%
h147970
 
4.9%
C147934
 
4.9%
S144339
 
4.8%
Other values (40)684595
22.9%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
NDF
119810 
US
59266 
FR
29601 
other
29070 
GB
28818 
Other values (7)
182436 

Length

Max length5
Median length2
Mean length2.461068015
Min length2

Characters and Unicode

Total characters1105022
Distinct characters20
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNDF
2nd rowNDF
3rd rowUS
4th rowother
5th rowUS
ValueCountFrequency (%)
NDF119810
26.7%
US59266
13.2%
FR29601
 
6.6%
other29070
 
6.5%
GB28818
 
6.4%
IT28790
 
6.4%
CA27914
 
6.2%
ES27840
 
6.2%
DE26934
 
6.0%
NL24974
 
5.6%
Other values (2)45984
 
10.2%
2021-05-17T20:09:52.131562image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ndf119810
26.7%
us59266
13.2%
fr29601
 
6.6%
other29070
 
6.5%
gb28818
 
6.4%
it28790
 
6.4%
ca27914
 
6.2%
es27840
 
6.2%
de26934
 
6.0%
nl24974
 
5.6%
Other values (2)45984
 
10.2%

Most occurring characters

ValueCountFrequency (%)
F149411
13.5%
D146744
13.3%
N144784
13.1%
S87106
 
7.9%
U83251
 
7.5%
E54774
 
5.0%
A51899
 
4.7%
T50789
 
4.6%
R29601
 
2.7%
o29070
 
2.6%
Other values (10)277593
25.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter959672
86.8%
Lowercase Letter145350
 
13.2%

Most frequent character per category

ValueCountFrequency (%)
F149411
15.6%
D146744
15.3%
N144784
15.1%
S87106
9.1%
U83251
8.7%
E54774
 
5.7%
A51899
 
5.4%
T50789
 
5.3%
R29601
 
3.1%
G28818
 
3.0%
Other values (5)132495
13.8%
ValueCountFrequency (%)
o29070
20.0%
t29070
20.0%
h29070
20.0%
e29070
20.0%
r29070
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1105022
100.0%

Most frequent character per script

ValueCountFrequency (%)
F149411
13.5%
D146744
13.3%
N144784
13.1%
S87106
 
7.9%
U83251
 
7.5%
E54774
 
5.0%
A51899
 
4.7%
T50789
 
4.6%
R29601
 
2.7%
o29070
 
2.6%
Other values (10)277593
25.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1105022
100.0%

Most frequent character per block

ValueCountFrequency (%)
F149411
13.5%
D146744
13.3%
N144784
13.1%
S87106
 
7.9%
U83251
 
7.5%
E54774
 
5.0%
A51899
 
4.7%
T50789
 
4.6%
R29601
 
2.7%
o29070
 
2.6%
Other values (10)277593
25.1%

Interactions

2021-05-17T20:08:26.044725image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:26.260343image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:26.467147image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:26.679576image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:26.895785image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:27.116094image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:27.337219image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:27.559366image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:27.982557image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:28.219674image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:28.490562image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:28.722571image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:28.947945image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:29.227600image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:29.465031image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:29.686754image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:29.902876image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:30.107252image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:30.319849image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:30.538778image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:30.753001image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:30.982036image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:31.199422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:31.403398image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:31.623092image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:31.837996image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:32.042443image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:32.252014image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:32.480438image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:32.687650image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:32.895175image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:33.101437image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:33.301815image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:33.505319image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:33.710202image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:33.914380image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:34.120938image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:34.336431image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:34.567296image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:34.796441image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:35.033192image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:35.267761image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:35.493740image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:35.855253image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:36.089991image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:36.310578image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:36.535708image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:36.751126image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:36.974710image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:37.231283image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:37.486999image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:37.719335image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:37.937903image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:38.160765image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:38.400110image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:38.617091image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:38.840977image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:39.065920image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:39.295752image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:39.511942image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:39.732775image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:39.952312image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:40.170382image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:40.385296image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:40.616869image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:40.832789image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:41.052336image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:41.256359image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:41.469043image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:41.683047image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:41.903533image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:42.141852image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:42.375682image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:42.588241image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:42.804486image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:43.036309image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:43.272939image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:43.518146image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:43.681743image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:43.856637image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:44.028443image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:44.205792image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:44.380262image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:44.546649image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:44.884707image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:45.090097image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:45.311867image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:45.534955image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:45.756466image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:46.007367image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:46.228927image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:46.439106image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:46.645640image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:46.854942image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:47.074215image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:47.298610image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:47.502806image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:47.660468image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:47.819460image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:47.981541image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:48.154674image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:48.341782image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:48.525227image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:48.708189image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:48.888861image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:49.055284image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:49.251006image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:49.454468image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:49.618350image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:49.774318image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:49.946556image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:50.130229image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:50.307906image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:50.475070image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:50.642525image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:50.809491image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:51.002327image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:51.217139image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:51.443177image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:51.654419image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:51.866727image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:52.077958image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:52.298638image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:52.510286image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:52.720561image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:52.929708image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:53.139561image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:53.376026image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:53.613516image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:53.859139image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:54.099964image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:54.324326image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:54.539997image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:54.749048image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:54.960811image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:55.179215image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:55.606271image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:55.815876image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:56.026325image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:56.241366image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:56.448709image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:56.659662image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:56.871176image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:57.117178image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:57.331477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:57.538418image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:57.750756image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:58.032602image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:58.247488image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:58.463869image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:58.674083image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:58.880152image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:59.091531image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:59.303303image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:59.526675image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:59.736802image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:08:59.958439image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:00.174509image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:00.383123image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:00.591931image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:00.808470image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:01.019019image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:01.237533image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:01.436042image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:01.639294image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:01.848499image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:02.110094image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:02.363622image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:02.571436image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:02.788608image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:02.993801image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:03.205164image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:03.430959image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:03.636496image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:03.845063image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:04.052402image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:04.255752image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:04.468790image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:04.697626image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:04.914579image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:05.138031image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:05.367898image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:05.579835image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:05.799647image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:06.025969image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:06.252598image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:06.471257image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:06.685902image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:06.901942image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:07.121058image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:07.339028image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:07.555946image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:07.775488image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:07.995637image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:08.211421image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:08.427672image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:08.645756image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:08.866127image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:09.086622image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:09.524122image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:09.745241image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:09.957971image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:10.194739image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:10.408185image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:10.614412image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:10.820472image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:11.021132image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:11.227852image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:11.434374image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:11.648776image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:11.855365image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:12.063120image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:12.266907image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:12.453916image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:12.610211image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:12.767251image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:12.954870image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:13.143533image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:13.299402image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:13.460570image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:13.622503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:13.786728image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:13.945504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:14.102718image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:14.263998image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:14.422834image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:14.580767image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:14.746286image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:14.906099image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-17T20:09:15.223218image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:15.384728image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:15.551542image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:15.713994image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:15.877248image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:16.030018image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:16.198571image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:16.415686image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:16.603585image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:16.783401image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:16.957095image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:17.117397image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:17.293031image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:17.483003image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:17.690556image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-17T20:09:18.140387image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-17T20:09:28.687756image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-17T20:09:29.984637image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:30.202114image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:30.415659image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:30.628987image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:30.836095image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-17T20:09:31.295183image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:31.507849image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-17T20:09:31.925101image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-17T20:09:32.829084image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-17T20:09:33.288814image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-17T20:09:33.708756image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:33.931108image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:34.161773image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:34.374635image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:34.591592image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:34.824758image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-17T20:09:35.246276image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:35.459385image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:35.656634image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:35.869619image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:36.092706image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:36.302975image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:36.516039image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:36.721213image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:36.924396image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:37.131406image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:37.337420image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:37.547228image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:37.791036image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:38.004570image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:38.213810image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:38.423171image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:38.627075image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:38.831692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-17T20:09:39.038736image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-05-17T20:09:52.268611image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-17T20:09:52.606826image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-17T20:09:52.949958image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-17T20:09:53.320517image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-17T20:09:53.707022image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-17T20:09:40.116725image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-17T20:09:42.184374image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

agesignup_flowdays_from_first_active_until_bookingdays_from_first_active_until_account_createddays_from_account_created_until_first_bookingyear_first_activemonth_first_activeday_first_activedayofweek_first_activeweekodyear_first_activeyear_first_bookingmonth_first_bookingday_first_bookingdayofweek_first_bookingweekofyear_first_bookingyear_first_created_accountmonth_first_created_accountday_first_created_accountdayofweek_first_created_accountweekofyear_first_created_accountgendersignup_methodlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destination
049073934666927200931931220296154242010628026-unknown-facebookendirectdirectuntrackedWebMac DesktopChromeNDF
138073287326596200952352120296154242011525221MALEfacebookenseogoogleuntrackedWebMac DesktopChromeNDF
2563419476-572009691242010820312010928139FEMALEbasicendirectdirectuntrackedWebWindows DesktopIEUS
34201043765278200910315442012985362011125049FEMALEfacebookendirectdirectuntrackedWebMac DesktopFirefoxother
4490101201011453201012553201011453-unknown-basicenotherotheromgWebMac DesktopChromeUS
546030320101255320101511201012553FEMALEbasicenothercraigslistuntrackedWebMac DesktopSafariUS
647010010201013653201011322201013653FEMALEbasicendirectdirectomgWebMac DesktopSafariUS
7500206020620101401201072933020101401FEMALEbasicenothercraigslistuntrackedWebMac DesktopSafariUS
8460000201014012010140120101401-unknown-basicenothercraigslistomgWebMac DesktopFirefoxUS
9360202201014012010162120101401FEMALEbasicenothercraigslistuntrackedWebMac DesktopFirefoxUS

Last rows

agesignup_flowdays_from_first_active_until_bookingdays_from_first_active_until_account_createddays_from_account_created_until_first_bookingyear_first_activemonth_first_activeday_first_activedayofweek_first_activeweekodyear_first_activeyear_first_bookingmonth_first_bookingday_first_bookingdayofweek_first_bookingweekofyear_first_bookingyear_first_created_accountmonth_first_created_accountday_first_created_accountdayofweek_first_created_accountweekofyear_first_created_accountgendersignup_methodlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destination
448991220101201392303920139252392013923039FEMALEbasicdesem-brandgoogleuntrackedWebWindows DesktopChromeother
44899232030320134275172013512182013427517-unknown-basicensem-brandgooglelinkedWebMac DesktopFirefoxother
44899341043043201211111452012122535120121111145MALEbasicendirectdirectuntrackedWebWindows DesktopChromeother
448994460202201310254432013102764320131025443MALEbasicenseofacebookuntrackedWebMac DesktopSafariother
4489956601801820133194112013461142013319411FEMALEbasicendirectdirectomgWebWindows DesktopFirefoxother
4489964921301320124184192012513212012418419-unknown-basicensem-non-brandgoogleomgWebMac DesktopSafariother
4489972825303201393338201397239201393338FEMALEbasicendirectdirectuntrackediOSiPhone-unknown-other
448998490000201310125412013101254120131012541-unknown-basicendirectdirectuntrackedWebMac DesktopSafariother
448999570000201211263472012112614820121126347FEMALEbasicendirectdirectuntrackedWebMac DesktopSafariother
449000290000201432841320143284132014328413MALEbasicendirectdirectuntrackedWebMac DesktopSafariother

Duplicate rows

Most frequent

agesignup_flowdays_from_first_active_until_bookingdays_from_first_active_until_account_createddays_from_account_created_until_first_bookingyear_first_activemonth_first_activeday_first_activedayofweek_first_activeweekodyear_first_activeyear_first_bookingmonth_first_bookingday_first_bookingdayofweek_first_bookingweekofyear_first_bookingyear_first_created_accountmonth_first_created_accountday_first_created_accountdayofweek_first_created_accountweekofyear_first_created_accountgendersignup_methodlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destinationcount
36252490000201452452120145256212014524521-unknown-basicendirectdirectproductWebWindows DesktopChromePT59
528374925550505505201452012120296154242014520121-unknown-basicendirectdirectuntrackediOSiPhone-unknown-NDF56
5281649255492054922014620232029615424201462023-unknown-basicendirectdirectuntrackediOSiPhone-unknown-NDF50
528464925551105511201451422020296154242014514220-unknown-basicendirectdirectuntrackediOSiPhone-unknown-NDF48
528474925551205512201451312020296154242014513120-unknown-basicendirectdirectuntrackediOSiPhone-unknown-NDF48
528664925552705527201442801820296154242014428018-unknown-basicendirectdirectuntrackediOSiPhone-unknown-NDF43
36357490000201452932220145293222014529322-unknown-basicendirectdirectuntrackedWebMac DesktopSafariPT42
5281549255491054912014631232029615424201463123-unknown-basicendirectdirectuntrackediOSiPhone-unknown-NDF40
528724925553205532201442321720296154242014423217-unknown-basicendirectdirectuntrackediOSiPhone-unknown-NDF40
33786490000201321107201321107201321107-unknown-basicendirectdirectuntrackedWebWindows DesktopChromePT38